{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y6iAxoENXbcB"
   },
   "source": [
    "# Initial Setups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_zoMiGFXbcC"
   },
   "source": [
    "## (Google Colab use only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "l4743KLAXbcD"
   },
   "outputs": [],
   "source": [
    "# Use Google Colab\n",
    "use_colab = False\n",
    "\n",
    "# Is this notebook running on Colab?\n",
    "# If so, then google.colab package (github.com/googlecolab/colabtools)\n",
    "# should be available in this environment\n",
    "\n",
    "# Previous version used importlib, but we could do the same thing with\n",
    "# just attempting to import google.colab\n",
    "try:\n",
    "    from google.colab import drive\n",
    "    colab_available = True\n",
    "except:\n",
    "    colab_available = False\n",
    "\n",
    "if use_colab and colab_available:\n",
    "    drive.mount('/content/drive/')\n",
    "\n",
    "    # cd to the appropriate working directory under my Google Drive\n",
    "    %cd '/content/drive/My Drive/cs696ds_lexalytics/Prompting Experiments'\n",
    "    \n",
    "    # Install packages specified in requirements\n",
    "    !pip install -r requirements.txt\n",
    "    \n",
    "    # List the directory contents\n",
    "    !ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IgXotFPyXbcE"
   },
   "source": [
    "## Experiment parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "xOZHQFaSXbcE",
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# We will use the following string ID to identify this particular (training) experiments\n",
    "# in directory paths and other settings\n",
    "experiment_id = 'prompt_logit_atsc_laptops_gpt-2_amazon_single_i_felt'\n",
    "\n",
    "# Random seed\n",
    "random_seed = 696\n",
    "\n",
    "# path to pretrained MLM model folder or the string \"gpt2\"\n",
    "lm_model_path = 'gpt2'\n",
    "#lm_model_path = 'gpt2_amazon'\n",
    "# Proportion to be reserved for validation\n",
    "validation_dataset_proportion = 0.2\n",
    "\n",
    "\n",
    "# Prompts to be added to the end of each review text\n",
    "# Note: pseudo-labels for each prompt should be given in the order of (positive), (negative), (neutral)\n",
    "sentiment_prompts = [\n",
    "    {\"prompt\": \" I felt the {aspect} was\", \"labels\": [\" good\", \" bad\", \" ok\"]},\n",
    "    {\"prompt\": \" The {aspect} made me feel\", \"labels\": [\" good\", \" bad\", \" indifferent\"]},\n",
    "    {\"prompt\": \" The {aspect} is \", \"labels\": [\" good\", \" bad\", \" ok\"]}\n",
    "]\n",
    "# sentiment_prompts = [\n",
    "#     {\"prompt\": \"I felt the {aspect} was \", \"labels\": [\"good\", \"bad\", \"ok\"]}\n",
    "# ]\n",
    "\n",
    "# Training settings\n",
    "training_epochs = 20\n",
    "training_batch_size = 15\n",
    "training_learning_rate = 2e-5\n",
    "training_weight_decay = 0.01\n",
    "training_warmup_steps_duration = 0.15\n",
    "training_hard_restart_num_cycles = 3\n",
    "training_max_grad_norm = 1.0\n",
    "training_best_model_criterion = 'train_loss'\n",
    "training_domain = 'laptops' # 'laptops', 'restaurants', 'joint'\n",
    "training_dataset_proportion = 1.0\n",
    "training_dataset_few_shot_size = None\n",
    "training_dataset_few_shot_size = None\n",
    "\n",
    "training_lm_freeze = False\n",
    "\n",
    "validation_enabled = False\n",
    "validation_dataset_proportion = 0.2\n",
    "validation_batch_size = 15\n",
    "\n",
    "testing_batch_size = 15\n",
    "testing_domain = 'laptops' # 'laptops', 'restaurants', 'joint'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batch size adjustment for multiple prompts.\n",
    "training_batch_size = training_batch_size // len(sentiment_prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "0Q8ZZYXrZgrJ"
   },
   "outputs": [],
   "source": [
    "print(\"Experiment ID:\", experiment_id)\n",
    "print(\"training_batch_size\", training_batch_size)\n",
    "print(\"sentiment_prompts\", sentiment_prompts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PHAAroOoXbcF"
   },
   "source": [
    "## Package imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G5vK1ItVXbcF",
    "outputId": "fcf294d6-882a-4bc5-8730-34b46d92efd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.10 (default, Feb 20 2021, 21:17:23) \n",
      "[GCC 7.5.0]\n",
      "NumPy version: 1.19.5\n",
      "PyTorch version: 1.7.1\n",
      "Transformers version: 4.3.3\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import random\n",
    "import shutil\n",
    "import copy\n",
    "import inspect\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import transformers\n",
    "import datasets\n",
    "import sklearn.metrics\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import tqdm\n",
    "\n",
    "current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parent_dir = os.path.dirname(current_dir)\n",
    "sys.path.append(parent_dir)\n",
    "\n",
    "import utils\n",
    "\n",
    "# Random seed settings\n",
    "random.seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "torch.manual_seed(random_seed)\n",
    "\n",
    "# cuBLAS reproducibility\n",
    "# https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility\n",
    "os.environ['CUBLAS_WORKSPACE_CONFIG'] = \":4096:8\"\n",
    "torch.set_deterministic(True)\n",
    "torch.manual_seed(random_seed)\n",
    "\n",
    "# Print version information\n",
    "print(\"Python version: \" + sys.version)\n",
    "print(\"NumPy version: \" + np.__version__)\n",
    "print(\"PyTorch version: \" + torch.__version__)\n",
    "print(\"Transformers version: \" + transformers.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CWxHZt1sXbcF"
   },
   "source": [
    "## PyTorch GPU settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z8rEYJstXbcG",
    "outputId": "51d4e208-0992-4073-af06-19c9210fbf18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Version: 10.2\n",
      "cuDNN Version: 7605\n",
      "CUDA Device Name: Tesla T4\n",
      "CUDA Capabilities: (7, 5)\n",
      "Number of CUDA devices: 1\n",
      "\n",
      "PyTorch device selected: cuda\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():    \n",
    "    torch_device = torch.device('cuda')\n",
    "\n",
    "    # Set this to True to make your output immediately reproducible\n",
    "    # Note: https://pytorch.org/docs/stable/notes/randomness.html\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    \n",
    "    # Disable 'benchmark' mode: Set this False if you want to measure running times more fairly\n",
    "    # Note: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "    # Faster Host to GPU copies with page-locked memory\n",
    "    use_pin_memory = True\n",
    "    \n",
    "    # Number of compute devices to be used for training\n",
    "    training_device_count = torch.cuda.device_count()\n",
    "\n",
    "    # CUDA libraries version information\n",
    "    print(\"CUDA Version: \" + str(torch.version.cuda))\n",
    "    print(\"cuDNN Version: \" + str(torch.backends.cudnn.version()))\n",
    "    print(\"CUDA Device Name: \" + str(torch.cuda.get_device_name()))\n",
    "    print(\"CUDA Capabilities: \"+ str(torch.cuda.get_device_capability()))\n",
    "    print(\"Number of CUDA devices: \"+ str(training_device_count))\n",
    "    \n",
    "else:\n",
    "    torch_device = torch.device('cpu')\n",
    "    use_pin_memory = False\n",
    "    \n",
    "    # Number of compute devices to be used for training\n",
    "    training_device_count = 1\n",
    "\n",
    "print()\n",
    "print(\"PyTorch device selected:\", torch_device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WGXXmQJrXbcG"
   },
   "source": [
    "# Prepare Datasets for Prompt-based Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJ0-SwejXbcG"
   },
   "source": [
    "## Load the SemEval dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MS52e_iWXbcH",
    "outputId": "7ab487ec-589b-4014-f59a-a3d966e5a719"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n",
      "Reusing dataset sem_eval2014_task4_dataset (/dataset_cache/sem_eval2014_task4_dataset/default-00707989ebea381d/0.0.1/f33ba7108331ad17be3f9fb710ca001edb383fba797c6ed0938354e6812ca969)\n",
      "Using custom data configuration default\n",
      "Reusing dataset sem_eval2014_task4_dataset (./dataset_cache/sem_eval2014_task4_dataset/default-2ba098669778f075/0.0.1/f33ba7108331ad17be3f9fb710ca001edb383fba797c6ed0938354e6812ca969)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # Load semeval for both domains\n",
    "    laptops_dataset = datasets.load_dataset(\n",
    "        os.path.abspath('../dataset_scripts/semeval2014_task4/semeval2014_task4.py'),\n",
    "        data_files={\n",
    "            'test': '../dataset_files/semeval_2014/Laptops_Test_Gold.xml',\n",
    "            'train': '../dataset_files/semeval_2014/Laptop_Train_v2.xml',\n",
    "        },\n",
    "        cache_dir='../dataset_cache')\n",
    "\n",
    "    restaurants_dataset = datasets.load_dataset(\n",
    "        os.path.abspath('../dataset_scripts/semeval2014_task4/semeval2014_task4.py'),\n",
    "        data_files={\n",
    "            'test': '../dataset_files/semeval_2014/Restaurants_Test_Gold.xml',\n",
    "            'train': '../dataset_files/semeval_2014/Restaurants_Train_v2.xml',\n",
    "        },\n",
    "        cache_dir='../dataset_cache')\n",
    "except:\n",
    "    # Load semeval for both domains\n",
    "    laptops_dataset = datasets.load_dataset(\n",
    "        os.path.abspath('../dataset_scripts/semeval2014_task4/semeval2014_task4.py'),\n",
    "        data_files={\n",
    "            'test': '../dataset_files/semeval_2014/Laptops_Test_Gold.xml',\n",
    "            'train': '../dataset_files/semeval_2014/Laptop_Train_v2.xml',\n",
    "        })\n",
    "\n",
    "    restaurants_dataset = datasets.load_dataset(\n",
    "        os.path.abspath('../dataset_scripts/semeval2014_task4/semeval2014_task4.py'),\n",
    "        data_files={\n",
    "            'test': '../dataset_files/semeval_2014/Restaurants_Test_Gold.xml',\n",
    "            'train': '../dataset_files/semeval_2014/Restaurants_Train_v2.xml',\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "R2BIX6O5XbcH"
   },
   "outputs": [],
   "source": [
    "# The dataset chosen for training/testing\n",
    "print(\"Training domain:\", training_domain)\n",
    "print(\"Testing domain:\", testing_domain)\n",
    "\n",
    "if training_domain == 'laptops':\n",
    "    train_set = laptops_dataset['train']\n",
    "elif training_domain == 'restaurants':\n",
    "    train_set = restaurants_dataset['train']\n",
    "elif training_domain == 'joint':\n",
    "    train_set = laptops_dataset['train'] + restaurants_dataset['train']\n",
    "\n",
    "if testing_domain == 'laptops':\n",
    "    test_set = laptops_dataset['test']\n",
    "elif testing_domain == 'restaurants':\n",
    "    test_set = restaurants_dataset['test']\n",
    "elif testing_domain == 'joint':\n",
    "    test_set = laptops_dataset['test'] + restaurants_dataset['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8Kfyt_SnXbcH"
   },
   "source": [
    "## Train-validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xRPivkujXbcH",
    "outputId": "2aee9326-06af-4a93-8c21-91f103d40f85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset size: 2313\n"
     ]
    }
   ],
   "source": [
    "# Training set size after validation split\n",
    "if validation_enabled:\n",
    "    new_train_dataset_size = int(len(train_set) * (1 - validation_dataset_proportion))\n",
    "    new_valid_dataset_size = len(train_set) - new_train_dataset_size\n",
    "    print(\"Training dataset after split:\", new_train_dataset_size)\n",
    "    print(\"Validation dataset after split:\", new_train_dataset_size)\n",
    "else:\n",
    "    new_train_dataset_size = len(train_set)\n",
    "    print(\"Training dataset size:\", new_train_dataset_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7rM8fxYbXbcI",
    "outputId": "7995e1cf-c963-4969-aa19-0a00d953dc15"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached shuffled indices for dataset at ./dataset_cache/sem_eval2014_task4_dataset/default-2ba098669778f075/0.0.1/f33ba7108331ad17be3f9fb710ca001edb383fba797c6ed0938354e6812ca969/cache-de8ce8ac91817d47.arrow\n"
     ]
    }
   ],
   "source": [
    "train_set = train_set.shuffle(seed=random_seed)\n",
    "\n",
    "new_train_set = train_set.select(indices=np.arange(new_train_dataset_size))\n",
    "\n",
    "if validation_enabled:\n",
    "    new_valid_set = train_set.select(\n",
    "        indices=np.arange(\n",
    "            new_train_dataset_size,\n",
    "            new_train_dataset_size + new_valid_dataset_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For few shot experiments, select first few examples in new_train_set for training\n",
    "if training_dataset_few_shot_size is not None:\n",
    "    new_train_set = new_train_set.select(indices=np.arange(training_dataset_few_shot_size))\n",
    "    \n",
    "    print(\"Few-shot training dataset size:\", len(new_train_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Abgkblm8XbcI",
    "outputId": "814467d0-9a8b-41b6-8aef-e80ba80b0567"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'aspect': 'loads', 'sentiment': 2, 'text': 'Once I removed all the software the laptop loads in 15-20 seconds.'}\n"
     ]
    }
   ],
   "source": [
    "print(new_train_set[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhyCQurHXbcI"
   },
   "source": [
    "# Zero-shot ATSC with Prompts + Logistic Regression Head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZPwL5kDVXbcJ"
   },
   "source": [
    "## Load the pretrained LM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "L5VyDL9SXbcJ"
   },
   "outputs": [],
   "source": [
    "# Load pretrained language model\n",
    "lm = transformers.AutoModelForCausalLM.from_pretrained(lm_model_path)\n",
    "try:\n",
    "    tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2', cache_dir='./gpt2_cache')\n",
    "except:\n",
    "    tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2')\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rBPKg6FPXbcJ"
   },
   "source": [
    "Define a new Model with CLM output head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IecBxF8-XbcK",
    "outputId": "35501dba-5714-49c5-f821-cbab22bcf9f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11274, 14774, 482]]\n",
      "Detected LM type: gpt2\n"
     ]
    }
   ],
   "source": [
    "# Encode the pseudo-label words for each sentiment class\n",
    "sentiment_word_ids = []\n",
    "\n",
    "for sp in sentiment_prompts:\n",
    "    sentiment_word_ids.append(\n",
    "        [tokenizer.convert_tokens_to_ids(tokenizer.tokenize(w))[0] for w in sp['labels']])\n",
    "\n",
    "print(sentiment_word_ids)\n",
    "\n",
    "\n",
    "classifier_model = utils.MultiPromptLogitSentimentClassificationHead(\n",
    "    lm=lm,\n",
    "    num_class=3,\n",
    "    num_prompts=len(sentiment_prompts),\n",
    "    pseudo_label_words=sentiment_word_ids,\n",
    "    target_token_id=tokenizer.eos_token_id)\n",
    "\n",
    "classifier_model = classifier_model.to(device=torch_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "S5RuhXlrzNKt"
   },
   "outputs": [],
   "source": [
    "# layers in classifier_model.lm should be frozen\n",
    "if training_lm_freeze:\n",
    "    for param in lm.parameters():\n",
    "        assert param.requires_grad == False\n",
    "\n",
    "    # Make sure that classifier_model.linear is trainable\n",
    "    for param in classifier_model.linear.parameters():\n",
    "        assert param.requires_grad == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "GzDhZYi_EjRu"
   },
   "outputs": [],
   "source": [
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    new_train_set, shuffle=True, batch_size=training_batch_size, pin_memory=use_pin_memory)\n",
    "\n",
    "if validation_enabled:\n",
    "    validation_dataloader = torch.utils.data.DataLoader(\n",
    "        new_valid_set, batch_size=validation_batch_size, pin_memory=use_pin_memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g5OsMm7JxL01",
    "outputId": "5343f219-a94c-4774-bc1a-73035fd30362"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There will be 46260 training steps.\n",
      "Warmup steps: 6939\n"
     ]
    }
   ],
   "source": [
    "# How many training steps would we have?\n",
    "training_total_steps = len(train_dataloader) * training_epochs\n",
    "\n",
    "print(\"There will be %d training steps.\" % training_total_steps)\n",
    "\n",
    "# Let's have warmups for the first (training_warmup_steps_duration)% of steps.\n",
    "training_warmup_steps = int(training_total_steps * training_warmup_steps_duration)\n",
    "\n",
    "print(\"Warmup steps:\", training_warmup_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TSQXFypiXbcK"
   },
   "source": [
    "## Training settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "VBKBor8fXbcK"
   },
   "outputs": [],
   "source": [
    "loss_function = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "no_decay = ['bias', 'LayerNorm.weight']\n",
    "\n",
    "optimizer_grouped_parameters = [\n",
    "    {'params': [p for n, p in classifier_model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': training_weight_decay},\n",
    "    {'params': [p for n, p in classifier_model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "    ]\n",
    "\n",
    "optimizer = transformers.AdamW(\n",
    "    optimizer_grouped_parameters,\n",
    "    lr=training_learning_rate,\n",
    "    weight_decay=training_weight_decay)\n",
    "\n",
    "scheduler = transformers.get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    num_warmup_steps=training_warmup_steps,\n",
    "    num_training_steps=training_total_steps\n",
    ")\n",
    "\n",
    "# The directory to save the best version of the head\n",
    "trained_model_directory = os.path.join('..', 'trained_models', experiment_id)\n",
    "\n",
    "shutil.rmtree(trained_model_directory, ignore_errors=True)\n",
    "os.makedirs(trained_model_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "x3Md8MvtXbcL"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(predictions, labels):\n",
    "    preds = predictions.argmax(-1)\n",
    "\n",
    "    precision, recall, f1, _ = sklearn.metrics.precision_recall_fscore_support(\n",
    "        y_true=labels, y_pred=preds, labels=[0,1,2], average='macro')\n",
    "\n",
    "    acc = sklearn.metrics.accuracy_score(labels, preds)\n",
    "\n",
    "    return {\n",
    "        'accuracy': acc,\n",
    "        'f1': f1,\n",
    "        'precision': precision,\n",
    "        'recall': recall\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VS14Req1XbcL"
   },
   "source": [
    "## Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "njxKcpgtXbcL",
    "outputId": "cb95c6ae-61ff-4e73-c323-87eb087d627d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae6b5e809189459ea43d35b1493002ef",
       "version_major": 2,
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 0\n",
      "\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 0, Training Loss: 0.522386372089386\n",
      "\n",
      "Training epoch 1\n",
      "\n"
     ]
    },
    {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1, Training Loss: 0.8361130952835083\n",
      "\n",
      "Training epoch 2\n",
      "\n"
     ]
    },
    {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2, Training Loss: 0.5099201202392578\n",
      "\n",
      "Training epoch 3\n",
      "\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3, Training Loss: 0.4909999370574951\n",
      "\n",
      "Training epoch 4\n",
      "\n"
     ]
    },
    {
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4, Training Loss: 0.5545147657394409\n",
      "\n",
      "Training epoch 5\n",
      "\n"
     ]
    },
    {
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     },
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5, Training Loss: 0.15714900195598602\n",
      "\n",
      "Training epoch 6\n",
      "\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 6, Training Loss: 0.5445879101753235\n",
      "\n",
      "Training epoch 7\n",
      "\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 7, Training Loss: 0.20934824645519257\n",
      "\n",
      "Training epoch 8\n",
      "\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 8, Training Loss: 0.35222020745277405\n",
      "\n",
      "Training epoch 9\n",
      "\n"
     ]
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     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 9, Training Loss: 0.27824389934539795\n",
      "\n",
      "Training epoch 10\n",
      "\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 10, Training Loss: 0.9314234256744385\n",
      "\n",
      "Training epoch 11\n",
      "\n"
     ]
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      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 11, Training Loss: 1.1626267433166504\n",
      "\n",
      "Training epoch 12\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d9a845ccffd433dac83fdc82ca19d5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 12, Training Loss: 0.18668684363365173\n",
      "\n",
      "Training epoch 13\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdaaa84d91a842f68212a6ebd4a2a74b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 13, Training Loss: 0.4875752925872803\n",
      "\n",
      "Training epoch 14\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9299a170ad7f4387b13d69fa2f8301ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 14, Training Loss: 1.0743637084960938\n",
      "\n",
      "Training epoch 15\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8b8b15d57d74bc28286cdc655734b6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 15, Training Loss: 0.13520470261573792\n",
      "\n",
      "Training epoch 16\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b6aa02dc4e34475bac8dc81cec0e507",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 16, Training Loss: 0.16830715537071228\n",
      "\n",
      "Training epoch 17\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15fad9cb8d75454e819f5b87b9a52b81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 17, Training Loss: 0.2449158877134323\n",
      "\n",
      "Training epoch 18\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97c9cf642bff437ca46b52c9b8be4833",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 18, Training Loss: 0.020504996180534363\n",
      "\n",
      "Training epoch 19\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab317e54a37c4d17ae413f49f8987ad3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2313.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 19, Training Loss: 0.413688600063324\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_epoch = -1\n",
    "\n",
    "if training_best_model_criterion in ('train_loss', 'valid_loss'):\n",
    "    best_score = float('inf')\n",
    "else:\n",
    "    best_score = -1\n",
    "\n",
    "for epoch in tqdm.notebook.tqdm(range(int(training_epochs))):\n",
    "\n",
    "    print(\"Training epoch %d\" % epoch)\n",
    "    print()\n",
    "\n",
    "    classifier_model.train()\n",
    "\n",
    "    for batch in tqdm.notebook.tqdm(train_dataloader):\n",
    "\n",
    "        reviews_repeated = []\n",
    "        prompts_populated = []\n",
    "\n",
    "        for prompt in sentiment_prompts:\n",
    "            reviews_repeated = reviews_repeated + batch[\"text\"]\n",
    "\n",
    "            for aspect in batch[\"aspect\"]:\n",
    "                prompts_populated.append(prompt['prompt'].format(aspect=aspect))\n",
    "\n",
    "        batch_encoded = tokenizer(\n",
    "            reviews_repeated, prompts_populated,\n",
    "            padding='max_length', truncation='only_first', max_length=256,\n",
    "            return_tensors='pt')\n",
    "        \n",
    "        batch_encoded = batch_encoded.to(torch_device)\n",
    "\n",
    "        batch_label = batch[\"sentiment\"]\n",
    "        batch_label = batch_label.to(torch_device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        batch_output = classifier_model(batch_encoded)\n",
    "\n",
    "        loss = loss_function(batch_output, batch_label)\n",
    "\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(classifier_model.parameters(), training_max_grad_norm)\n",
    "\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        \n",
    "    print(\"Epoch {}, Training Loss: {}\".format(epoch, loss.item()))\n",
    "    print()\n",
    "\n",
    "    # Validate the model using val dataset\n",
    "    if validation_enabled:\n",
    "        with torch.no_grad():\n",
    "            classifier_model.eval()\n",
    "\n",
    "            print(\"Validation epoch %d\" % epoch)\n",
    "            print()\n",
    "\n",
    "            predictions_val = torch.Tensor()\n",
    "            labels_val = torch.Tensor()\n",
    "\n",
    "            for batch_val in tqdm.notebook.tqdm(validation_dataloader):\n",
    "\n",
    "                reviews_repeated = []\n",
    "                prompts_populated = []\n",
    "\n",
    "                for prompt in sentiment_prompts:\n",
    "                    reviews_repeated = reviews_repeated + batch_val[\"text\"]\n",
    "\n",
    "                    for aspect in batch_val[\"aspect\"]:\n",
    "                        prompts_populated.append(prompt['prompt'].format(aspect=aspect))\n",
    "\n",
    "                batch_val_encoded = tokenizer(\n",
    "                    reviews_repeated, prompts_populated,\n",
    "                    padding='max_length', truncation='only_first', max_length=256,\n",
    "                    return_tensors='pt')\n",
    "\n",
    "                batch_val_encoded.to(torch_device)\n",
    "\n",
    "                batch_val_label = batch_val[\"sentiment\"]\n",
    "\n",
    "                batch_val_output = classifier_model(batch_val_encoded)\n",
    "\n",
    "                batch_val_output = batch_val_output.to('cpu')\n",
    "\n",
    "                predictions_val = torch.cat([predictions_val, batch_val_output])\n",
    "                labels_val = torch.cat([labels_val, batch_val_label])\n",
    "\n",
    "            # Compute metrics\n",
    "            validation_loss = torch.nn.functional.cross_entropy(predictions_val, labels_val.long())\n",
    "            validation_metrics = compute_metrics(predictions_val, labels_val)\n",
    "\n",
    "            print(\"Validation Loss: {}, Validation Metrics: {}\".format(validation_loss.item(), validation_metrics))\n",
    "            print()\n",
    "\n",
    "    if training_best_model_criterion == 'train_loss':\n",
    "        epoch_score = loss.item()\n",
    "    elif training_best_model_criterion == 'valid_loss':\n",
    "        epoch_score = validation_loss.item()\n",
    "        \n",
    "    if training_best_model_criterion in ('train_loss', 'valid_loss'):\n",
    "        better_model_found = epoch_score < best_score\n",
    "    else:\n",
    "        better_model_found = epoch_score > best_score\n",
    "\n",
    "    # Save the current epoch's model if the validation loss is lower than the best known so far\n",
    "    if better_model_found:\n",
    "        if best_epoch != -1:\n",
    "            try:\n",
    "                os.remove(os.path.join(trained_model_directory, 'epoch_{}.pt'.format(best_epoch)))\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "        best_score = epoch_score\n",
    "        best_epoch = epoch\n",
    "\n",
    "        torch.save(\n",
    "            classifier_model.state_dict(),\n",
    "            os.path.join(trained_model_directory, 'epoch_{}.pt'.format(epoch)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r5grOXDoXbcM"
   },
   "source": [
    "## Evaluation with in-domain test set\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "jZoouZfHXbcM"
   },
   "outputs": [],
   "source": [
    "test_dataloader = torch.utils.data.DataLoader(\n",
    "    test_set, batch_size=testing_batch_size, pin_memory=use_pin_memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "amsr_4J25tPN"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100,
     "referenced_widgets": [
      "49b354fb641b4348bb3aa5c2ce54e35b",
      "6f1f229e13f049778451cc8c60ab31a4",
      "77eaf2321133478cbe4d7870fd9ece40",
      "2e183fcbbaa24726a0a9205dbb241bf0",
      "4611050c6a024549ad6dae9390e18df1",
      "e3b9622168e4450590e919fec8302226",
      "8d5fca8f8c834a5b90f9af83fe32f580",
      "5a34a4ee3cc948ba82a5aaf63a5739c4"
     ]
    },
    "id": "Y5CAEehjXbcM",
    "outputId": "46f7daa7-57c6-4543-9d2e-779742436fe8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading epoch 18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49b354fb641b4348bb3aa5c2ce54e35b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=638.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'accuracy': 0.7884012539184952, 'f1': 0.7493440118659409, 'precision': 0.7557809330628803, 'recall': 0.7647720675209819}\n"
     ]
    }
   ],
   "source": [
    "# Load the best found head weights\n",
    "with torch.no_grad():\n",
    "    print('Loading epoch {}'.format(best_epoch))\n",
    "\n",
    "    classifier_model.load_state_dict(torch.load(\n",
    "        os.path.join(trained_model_directory, 'epoch_{}.pt'.format(best_epoch)),\n",
    "        map_location=torch_device))\n",
    "\n",
    "    classifier_model.eval()\n",
    "\n",
    "    predictions_test = torch.Tensor()\n",
    "    labels_test = torch.Tensor()\n",
    "\n",
    "    for batch_test in tqdm.notebook.tqdm(test_dataloader):\n",
    "\n",
    "        reviews_repeated = []\n",
    "        prompts_populated = []\n",
    "\n",
    "        for prompt in sentiment_prompts:\n",
    "            reviews_repeated = reviews_repeated + batch_test[\"text\"]\n",
    "\n",
    "            for aspect in batch_test[\"aspect\"]:\n",
    "                prompts_populated.append(prompt['prompt'].format(aspect=aspect))\n",
    "\n",
    "        batch_test_encoded = tokenizer(\n",
    "            reviews_repeated, prompts_populated,\n",
    "            padding='max_length', truncation='only_first', max_length=256,\n",
    "            return_tensors='pt')\n",
    "\n",
    "        batch_test_encoded.to(torch_device)\n",
    "\n",
    "        batch_test_label = batch_test[\"sentiment\"]\n",
    "\n",
    "        batch_test_output = classifier_model(batch_test_encoded)\n",
    "\n",
    "        batch_test_output = batch_test_output.to('cpu')\n",
    "\n",
    "        predictions_test = torch.cat([predictions_test, batch_test_output])\n",
    "        labels_test = torch.cat([labels_test, batch_test_label])\n",
    "\n",
    "    # Compute metrics\n",
    "    test_metrics = compute_metrics(predictions_test, labels_test)\n",
    "\n",
    "    print(test_metrics)\n",
    "    \n",
    "    # Save test_metrics into a file for later processing\n",
    "    with open(os.path.join(trained_model_directory, 'test_metrics.json'), 'w') as test_metrics_json:\n",
    "        json.dump(test_metrics, test_metrics_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if best_epoch != -1:\n",
    "    try:\n",
    "        os.remove(os.path.join(trained_model_directory, 'epoch_{}.pt'.format(best_epoch)))\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A9lF3BbyXbcN"
   },
   "source": [
    "## Results visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "_HpCXtbQXbcN",
    "outputId": "5ba2cd59-e8d1-480d-fc65-443e507f5c62"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate metrics and confusion matrix based upon predictions and true labels\n",
    "cm = sklearn.metrics.confusion_matrix(labels_test.detach().numpy(), predictions_test.detach().numpy().argmax(-1))\n",
    "\n",
    "df_cm = pd.DataFrame(\n",
    "    cm,\n",
    "    index=[i for i in [\"positive\", \"negative\", \"neutral\"]],\n",
    "    columns=[i for i in [\"positive\", \"negative\", \"neutral\"]])\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "ax = sn.heatmap(df_cm, annot=True)\n",
    "\n",
    "ax.set(xlabel='Predicted Label', ylabel='True Label')\n",
    "plt.show()"
   ]
  }
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